A Vision-Based Automatic Landing Method for Fixed-Wing UAVs

In this paper, a vision-based landing system for small-size fixed-wing unmanned aerial vehicles (UAVs) is presented. Since a single GPS without a differential correction typically provide position accuracy of at most a few meters, an airplane equipped with a single GPS only is not guaranteed to land at a designated location with a sufficient accuracy. Therefore, a visual servoing algorithm is proposed to improve the accuracy of landing. In this scheme, the airplane is controlled to fly into the visual marker by directly feeding back the pitch and yaw deviation angles sensed by the forward-looking camera during the terminal landing phase. The visual marker is a monotone hemispherical airbag, which serves as the arresting device while providing a strong and yet passive visual cue for the vision system. The airbag is detected by using color- and moment-based target detection methods. The proposed idea was tested in a series of experiments using a blended wing-body airplane and proven to be viable for landing of small fixed-wing UAVs.     

Vision-Based Odometry and SLAM for Medium and High Altitude Flying UAVs

This paper proposes vision-based techniques for localizing an unmanned aerial vehicle (UAV) by means of an on-board camera. Only natural landmarks provided by a feature tracking algorithm will be considered, without the help of visual beacons or landmarks with known positions. First, it is described a monocular visual odometer which could be used as a backup system when the accuracy of GPS is reduced to critical levels. Homography-based techniques are used to compute the UAV relative translation and rotation by means of the images gathered by an onboard camera. The analysis of the problem takes into account the stochastic nature of the estimation and practical implementation issues. The visual odometer is then integrated into a simultaneous localization and mapping (SLAM) scheme in order to reduce the impact of cumulative errors in odometry-based position estimation approaches. Novel prediction and landmark initialization for SLAM in UAVs are presented. The paper is supported by an extensive experimental work where the proposed algorithms have been tested and validated using real UAVs.     

Incremental Focus of Attention for Robust Vision-Based Tracking

We present the Incremental Focus of Attention (IFA) architecture for robust, adaptive, real-time motion tracking. IFA systems combine several visual search and vision-based tracking algorithms into a layered hierarchy. The architecture controls the transitions between layers and executes algorithms appropriate to the visual environment at hand: When conditions are good, tracking is accurate and precise; as conditions deteriorate, more robust, yet less accurate algorithms take over; when tracking is lost altogether, layers cooperate to perform a rapid search for the target and continue tracking.Implemented IFA systems are extremely robust to most common types of temporary visual disturbances. They resist minor visual perturbances and recover quickly after full occlusions, illumination changes, major distractions, and target disappearances. Analysis of the algorithm's recovery times are supported by simulation results and experiments on real data. In particular, examples show that recovery times after lost tracking depend primarily on the number of objects visually similar to the target in the field of view.     

基于视觉的多点触摸基本技术实现方法

高性能的图像处理技术是基于视觉的多点触摸技术的关键,对于特征点的检测和跟踪是完成多点触摸系统的基础.文中主要介绍了几种算法用来解决特征点的识别和跟踪的问题.首先,图像轮廓变换算法用来分析手指与屏幕的接触区域,以及在此基础上采用中心计算算法确定特征点的中心坐标;再次,最小距离优先(MDF)算法对在两个相邻图像中的相应的特征点进行识别和跟踪,为随后的事件检测和手势识别提供了前提条件.实验结果表明,算法的性能满足多点触摸系统的实时要求.     

A Color Vision-Based Lane Tracking System for Autonomous Driving on Unmarked Roads

This work describes a color Vision-based System intended to perform stable autonomous driving on unmarked roads. Accordingly, this implies the development of an accurate road surface detection system that ensures vehicle stability. Although this topic has already been documented in the technical literature by different research groups, the vast majority of the already existing Intelligent Transportation Systems are devoted to assisted driving of vehicles on marked extra urban roads and highways. The complete system was tested on the BABIECA prototype vehicle, which was autonomously driven for hundred of kilometers accomplishing different navigation missions on a private circuit that emulates an urban quarter. During the tests, the navigation system demonstrated its robustness with regard to shadows, road texture, and weather and changing illumination conditions.     

A Catadioptric and Pan-Tilt-Zoom Camera Pair Object Tracking System for UAVs

Unmanned aerial vehicles (UAVs) are seeing widespread use in military, scientific, and civilian sectors in recent years. As the mission demands increase, these systems are becoming more complicated. Omnidirectional camera is a vision sensor that can captures 360° view in a single frame. In recent years omnidirectional camera usage has experienced a remarkable increase in many fields, where many innovative research has been done. Although, it is very promising, employment of omnidirectional cameras in UAVs is quite new. In this paper, an innovative sensory system is proposed, that has an omnidirectional imaging device and a pan tilt zoom (PTZ) camera. Such a system combines the advantages of both of the camera systems. The system can track any moving object within its 360° field of view and provide detailed images of it. The detection of the moving object has been accomplished by an adaptive background subtraction method implemented on the lowered resolution images of the catadioptric camera. A novel algorithm has also been developed to estimate the relative distance of the object with respect to the UAV, using tracking information of both of the cameras. The algorithms are implemented on an experimental system to validate the approach.     

Multiresolution Hierarchical Path-Planning for Small UAVs Using Wavelet Decompositions

We present an algorithm for solving the shortest (collision-free) path planning problem for an agent (e.g., a small UAV) with limited on-board computational resources. The agent has detailed knowledge of the environment and the obstacles only in the vicinity of its current position. Far away obstacles are only partially known and may even change dynamically. The algorithm makes use of the wavelet transform to construct an approximation of the environment at different levels of resolution. We associate with this multiresolution representation of the environment a graph, whose dimension can be made commensurate to the on-board computational resources of the agent. The adjacency list of the graph can be efficiently constructed directly from the approximation and detail wavelet coefficients, thus further speeding up the whole process. Simulations are presented to test the efficiency of the algorithm using non-trivial scenarios.     

A Picking Strategy for Circular Conveyor Tracking

In modern assembly lines, one of the most relevant issues is the automatic part feeding. This task consists in the separation of parts delivered in bulk and their presentation in a certain amount and orientation at the pick-up location. The traditional vibratory bowl feeder can no longer be adopted for the modern plants which require high velocity and increasing flexibility. In this paper, several types of feeders have been indentified and the Ars FlexiBowl has been classified as one of the most innovative. In order to take full advantage from this rotating feeder, a proper path planning technique is proposed for a generic manipulator. Such an approach consists mainly of two algorithms: one for prediction of moving part position, and one for circular conveyor tracking. These algorithms can be implemented into the robot control unit by exploiting the internal trajectory planner. This strategy has been implemented into an Adept robotic workcell, which has been adopted as an experimental setup. Such an application proves the usefulness of the proposed algorithms.     

Sky Region Obstacle Detection and Tracking for Vision-Based UAS Sense and Avoid

A customized detection and tracking algorithm for vision-based non cooperative UAS sense and avoid aimed at obstacles approaching from above the horizon is presented in this paper. The proposed approach comprises two main steps. Specifically, the first processing step is relevant to obstacle detection and tentative tracking for track confirmation and is based on top-hat and bottom-hat morphological filtering, local image analysis for a limited set of regions of interest, and multi-frame processing in stabilized coordinates. Once firm tracking is achieved, template matching and state estimation based on Kalman filtering are used to track the intruder aircraft and estimate its angular position and velocity. An extensive experimental analysis is presented which is based on a large set of flight data gathered in realistic near collision scenarios, in different operating conditions in terms of weather and illumination, and adopting different navigation units onboard the ownship. In particular, the focus is set on flight segments at a range between 3 km and 1.3 km, since the major interest is in understanding algorithm potential for relatively large time to collision. System performance is evaluated in terms of declaration range, probability of correct declaration, average number of false positives, tracking accuracy (angles and angular rates in a stabilized North-East-Down reference frame) and robustness with respect to track loss phenomena. Promising results are achieved regarding the trade-off between declaration range and false alarm probability, while the onboard navigation unit is found to heavily impact tracking accuracy.     

Real-Time Target Tracking for Autonomous UAVs in Adversarial Environments: A Gradient Search Algorithm

This paper presents a rule-based intelligent guidance strategy for autonomous pursuit of mobile targets by unmanned aerial vehicles (UAVs) in an area with threats, obstacles, and restricted regions. The probabilistic threat exposure map (PTEM) is used as the mathematical formulation of the area of operation for the guidance strategy to make intelligent decisions based on a set of defined rules. The rules are developed for three objectives in the order of priority as: 1) avoid obstacles/restricted regions; 2) maintain the target proximity; 3) minimize UAV threat exposure level. A least-square estimation and kinematic relations are used to estimate/predict the target states based on noisy position measurements. The work presented herein addresses the same problem as in a previous work by the authors, and aims at improving the computational efficiency without compromising the performance. Simulation results of several pursuit scenarios demonstrate the full capabilities of the strategy and the improvement over the previous work     

A 3D Collision Avoidance Strategy for UAVs in a Non-Cooperative Environment

This paper presents a feasible 3D collision avoidance approach for fixed-wing unmanned aerial vehicles (UAVs). The proposed strategy aims to achieve the desired relative bearing in the horizontal plane and relative elevation in the vertical plane so that the host aircraft is able to avoid collision with the intruder aircraft in 3D. The host aircraft will follow a desired trajectory in the collision avoidance course and resume the pre-arranged trajectory after collision is avoided. The approaching stopping condition is determined for the host aircraft to trigger an evasion maneuver to avoid collision in terms of measured heading. A switching controller is designed to achieve the spatial collision avoidance strategy. Simulation results demonstrate that the proposed approach can effectively avoid spatial collision, making it suitable for integration into flight control systems of UAVs.     

机器人视觉跟踪轨迹的分级规划方法

针对跟踪轨迹规划对于确保得到连续光滑的跟踪运动的重要性,提出了两级视觉跟踪轨迹规划方法。第一阶段在图像平面上规划运动轨迹,在图像平面上得到的离散规划点映射到机器人关节空间。第二阶段在机器人关节空间中用三次样条函数来连接这些离散点。为了满足实时控制的要求,在图像处理过程中采用窗口技术并抽取边缘特征。建立用于跟踪两维平面运动物体(如随运输带运动的物体)的机器人视觉跟踪控制系统。实验结果表明,跟踪误差渐近地减小到允许的数值范围,所提出的跟踪轨迹规划方法是有效的。     

Adversarial Ground Target Tracking Using UAVs with Input Constraints

This paper deals with the problem of adversarial ground target tracking using Unmanned Aerial Vehicles (UAVs) subject to input constraints. For adversarial ground target tracking, tracking performance and UAV safety are two important considerations during tracking controller design. In this paper, a bang-bang heading rate controller is proposed to achieve circular tracking around the target. Exposure avoidance of the UAV to the target and minimizing the exposure time are studied respectively in terms of the initial state of the UAV. The performance of the proposed controller in both cases is also analyzed. Simulation results demonstrate the effectiveness of the proposed approach.     

Cooperative Tracking a Moving Target Using Multiple Fixed-wing UAVs

A cooperative tracking scheme is presented in this paper for multiple fixed-wing unmanned aerial vehicles (UAVs) to track an uncooperative, moving target. It is comprised of a target loitering algorithm and a formation flight algorithm. The loitering algorithm enables a constant speed UAV to circle around a moving target, whose speed is allowed to vary up to the UAV’s speed. The formation algorithm enables cooperative tracking using multiple UAVs by keeping them flying in a circular formation with equal inter-vehicle angular separation. Under this formation algorithm, the formation center can be controlled independently to perform target loitering, and the admissible range of the target’s speed would not be affected for given UAVs. The performance of the proposed tracking system is verified in numerical simulations.     

Decentralised Standoff Tracking of Moving Targets Using Adaptive Sliding Mode Control for UAVs

This paper proposes a decentralised vector field guidance algorithm for coordinated standoff tracking of a ground moving target by multiple UAVs. In particular, this study introduces additional adaptive terms in an existing sliding mode control concept for standoff tracking guidance, in order to reduce the effect of unmodelled dynamics and disturbances. Decentralised angular separation control between UAVs, in conjunction with decentralised estimation, is also introduced using either velocity or orbit radius change by different information/communication structures. Numerical simulations are performed to verify the feasibility and benefits of the proposed approach under a realistic ground vehicle tracking scenario, using multiple UAVs having unknown parameters in the heading-hold autopilot.     

A Parallel Particle Tracking Framework for Applications in Scientific Computing

Particle tracking methods are a versatile computational technique central to the simulation of a wide range of scientific applications. In this paper, we present a new parallel particle tracking framework for the applications of scientific computing. The framework includes the in-element particle tracking method, which is based on the assumption that particle trajectories are computed by problem data localized to individual elements, as well as the dynamic partitioning of particle-mesh computational systems. The ultimate goal of this research is to develop a parallel in-element particle tracking framework capable of interfacing with a different order of accuracy of ordinary differential equation (ODE) solver. The parallel efficiency of such particle-mesh systems depends on the partitioning of both the mesh elements and the particles; this distribution can change dramatically because of movement of the particles and adaptive refinement of the mesh. To address this problem we introduce a combined load function that is a function of both the particle and mesh element distributions. We present experimental results that detail the performance of this parallel load balancing approach for a three-dimensional particle-mesh test problem on an unstructured, adaptive mesh, and demonstrate the ability of interfacing with different ODE solvers.     

Stochastic Optimal Coordination of Small UAVs for Target Tracking using Regression-based Dynamic Programming

We study the problem of optimally coordinating multiple fixed-wing UAVs to perform vision-based target tracking, which entails that the UAVs are tasked with gathering the best joint vision-based measurements of an unpredictable ground target. We utilize an analytic expression for the error covariance associated with the fused measurements of the target’s position, and we employ stochastic fourth-order models for all vehicles, thereby incorporating a high degree of realism into the problem formulation. While dynamic programming can generate an optimal control policy that minimizes the expected value of the fused geolocation error covariance over time, it is accompanied by significant computational challenges due to the curse of dimensionality. In order to circumvent this challenge, we present a novel policy generation technique that combines simulation-based policy iteration with a robust regression scheme. The resulting control policy offers a significant advantage over alternative approaches and shows that the optimal control strategy involves coordinating the UAVs’ distances to the target rather than their viewing angles, which had been a common practice in target tracking.     

随机网格回归Monte Carlo双UAVs最优目标协调跟踪

针对多个无人机(unmanned aerial vehicle,UAV)执行基于视觉的目标跟踪的最佳协调问题,提高不可预知的地面目标的最佳结合点的视觉测量效果,提出了一种基于随机网格回归Monte Carlo的UAV最优目标跟踪策略。首先,通过无人机动力学和目标动力学分析,获得双UAV情况下的随机最优协调控制目标;其次,针对提出的控制目标,引入Monte Carlo求解方案,同时为解决标准Monte Carlo方案中存在的状态空间维度较高,计算复杂且精度不高的问题,利用随机网格方式构建回归Monte Carlo方案,实现UAV的最优协调控制;最后,通过仿真实验验证了所提方法的有效性。